Topology-Driven Anti-Entanglement Control for Soft Robots
Read the original on arXiv AI →The paper introduces a topology-driven Multi-Agent Reinforcement Learning (TD-MARL) framework designed to coordinate soft robots in precision manufacturing tasks, specifically to prevent entanglement during unwinding operations in highly constrained environments. By employing centralized learning with a shared topological state, the approach improves observability and training stability, while distributed execution reduces communication demands and enhances system reliability. Simulation results demonstrate that TD-MARL outperforms current deep reinforcement learning methods in convergence speed and anti-winding effectiveness.
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